[ADD] Extra visualization example #189
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ravinkohli
reviewed
Apr 28, 2021
ravinkohli
reviewed
Apr 28, 2021
ravinkohli
reviewed
Apr 28, 2021
Co-authored-by: Ravin Kohli <[email protected]>
Co-authored-by: Ravin Kohli <[email protected]>
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Thanks a lot for the suggestions, I have committed them! |
ravinkohli
approved these changes
Apr 29, 2021
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Actually could you also change the version here |
…rivera/Auto-PyTorch into refactor_development_extradocs
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Really nice example :) |
ravinkohli
reviewed
May 1, 2021
Co-authored-by: Ravin Kohli <[email protected]>
nabenabe0928
requested changes
May 5, 2021
docs/manual.rst
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| There are additional recommendations on how to interact with the API, further below in this manual. | ||
| However, you are welcome to contribute to this documentation by making a Pull-Request. | ||
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| In a nutshell, Auto-PyTorch searches for the best ensemble of both traditional machine learning models and neural networks for a given dataset. It does so via the `search()` method of the different supported task. Currently we support Tabular classification and Tabular Regression. We plan to also support image processing. |
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correction
The searching starts by calling search() function of each supported task.
Currently, we are supporting Tabular classification and Tabular Regression.
We expand the support to image processing tasks in the future.
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Great that you are saying method rather than function precisely!
Many people make mistakes:(
(function defined in an instance is called method and that in a class is called function)
docs/manual.rst
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| Resource Allocation | ||
| =================== | ||
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| Auto-PyTorch allows to control the maximum allowed resident set memory that an estimator can use. By providing the `memory_limit` argument to the `search()` method, one can make sure that neither the individual machine learning models fitted by SMAC nor the final ensemble consume more than `memory_limit` megabytes. |
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What is resident set memory?
docs/manual.rst
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| Auto-PyTorch allows to control the maximum allowed resident set memory that an estimator can use. By providing the `memory_limit` argument to the `search()` method, one can make sure that neither the individual machine learning models fitted by SMAC nor the final ensemble consume more than `memory_limit` megabytes. | ||
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| Additionally, one can control the allocated time to search for a model, via the argument `total_walltime_limit` to the `search()` method. This argument controls the total time SMAC can use to search for new configurations. The more time is allocated, the better the final estimator will be. |
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no need for the comma
search for a model, via the argument
==> search for a model via the argument
docs/manual.rst
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| to build an ensemble based on the models’ prediction for the validation set. The following hyperparameters control how the ensemble is constructed: | ||
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| * ``ensemble_size`` determines the maximal size of the ensemble. If it is set to zero, no ensemble will be constructed. | ||
| * ``ensemble_nbest`` allows the user to directly specify the number of models considered for the ensemble. This hyperparameter can be an integer *n*, such that only the best *n* models are used in the final ensemble. If a float between 0.0 and 1.0 is provided, ``ensemble_nbest`` would be interpreted as a fraction suggesting the percentage of models to use in the ensemble building process (namely, if ensemble_nbest is a float, library pruning is implemented as described in `Caruana et al. (2006) <https://dl.acm.org/doi/10.1109/ICDM.2006.76>`_). |
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This hyperparameter can be an integer n, such that only the best n models are used in the final ensemble
When an integer is provided for this hyperparameter, the final ensemble chooses each predictor from only the best n models
If a float between 0.0 and 1.0 is provided,
ensemble_nbestwould be interpreted as a fraction suggesting the percentage of models to use in the ensemble building process
If a float between 0.0 and 1.0 is provided, ensemble_nbest will be interpreted as a fraction suggesting the percentage of models to use in the ensemble building process.
For example, when we train 10 models before the ensemble building process and the hyperparameter is 0.7, we build an ensemble by taking some models from only best (?) 7 models among 10 models.
docs/manual.rst
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| * ``ensemble_size`` determines the maximal size of the ensemble. If it is set to zero, no ensemble will be constructed. | ||
| * ``ensemble_nbest`` allows the user to directly specify the number of models considered for the ensemble. This hyperparameter can be an integer *n*, such that only the best *n* models are used in the final ensemble. If a float between 0.0 and 1.0 is provided, ``ensemble_nbest`` would be interpreted as a fraction suggesting the percentage of models to use in the ensemble building process (namely, if ensemble_nbest is a float, library pruning is implemented as described in `Caruana et al. (2006) <https://dl.acm.org/doi/10.1109/ICDM.2006.76>`_). | ||
| * ``max_models_on_disc`` defines the maximum number of models that are kept on the disc, as a mechanism to control the amount of disc space consumed by *auto-sklearn*. Throughout the automl process, different individual models are optimized, and their predictions (and other metadata) is stored on disc. The user can set the upper bound on how many models are acceptable to keep on disc, yet this variable takes priority in the definition of the number of models used by the ensemble builder (that is, the minimum of ``ensemble_size``, ``ensemble_nbest`` and ``max_models_on_disc`` determines the maximal amount of models used in the ensemble). If set to None, this feature is disabled. |
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the maximum number of models that are kept on the disc
the maximum number of models that can stay on the disc
the amount of disc space consumed by auto-sklearn
auto-pytorch?
their predictions (and other metadata) is stored on disc.
are stored
nabenabe0928
reviewed
May 6, 2021
| os.environ['JOBLIB_TEMP_FOLDER'] = tmp.gettempdir() | ||
| os.environ['OMP_NUM_THREADS'] = '1' | ||
| os.environ['OPENBLAS_NUM_THREADS'] = '1' | ||
| os.environ['MKL_NUM_THREADS'] = '1' |
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What about those environment variables?
We need explanations for them.
…rivera/Auto-PyTorch into refactor_development_extradocs
nabenabe0928
approved these changes
May 7, 2021
franchuterivera
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to franchuterivera/Auto-PyTorch
that referenced
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Jun 14, 2021
* New refactor code. Initial push * Allow specifying the network type in include (automl#78) * Allow specifying the network type in include * Fix test flake 8 * fix test api * increased time for func eval in cros validation * Addressed comments Co-authored-by: Ravin Kohli <[email protected]> * Search space update (automl#80) * Added Hyperparameter Search space updates * added test for search space update * Added Hyperparameter Search space updates * added test for search space update * Added hyperparameter search space updates to network, trainer and improved check for search space updates * Fix mypy, flake8 * Fix tests and silly mistake in base_pipeline * Fix flake * added _cs_updates to dummy component * fixed indentation and isinstance comment * fixed silly error * Addressed comments from fransisco * added value error for search space updates * ADD tests for setting range of config space * fic utils search space update * Make sure the performance of pipeline is at least 0.8 * Early stop fixes * Network Cleanup (automl#81) * removed old supported_tasks dictionary from heads, added some docstrings and some small fixes * removed old supported_tasks attribute and updated doc strings in base backbone and base head components * removed old supported_tasks attribute from network backbones * put time series backbones in separate files, add doc strings and refactored search space arguments * split image networks into separate files, add doc strings and refactor search space * fix typo * add an intial simple backbone test similar to the network head test * fix flake8 * fixed imports in backbones and heads * added new network backbone and head tests * enabled tests for adding custom backbones and heads, added required properties to base head and base backbone * First documentation * Default to ubuntu-18.04 * Comment enhancements * Feature preprocessors, Loss strategies (automl#86) * ADD Weighted loss * Now? * Fix tests, flake, mypy * Fix tests * Fix mypy * change back sklearn requirement * Assert for fast ica sklearn bug * Forgot to add skip * Fix tests, changed num only data to float * removed fast ica * change num only dataset * Increased number of features in num only * Increase timeout for pytest * ADD tensorboard to requirement * Fix bug with small_preprocess * Fix bug in pytest execution * Fix tests * ADD error is raised if default not in include * Added dynamic search space for deciding n components in feature preprocessors, add test for pipeline include * Moved back to random configs in tabular test * Added floor and ceil and handling of logs * Fix flake * Remove TruncatedSVD from cs if num numerical ==1 * ADD flakyness to network accuracy test * fix flake * remove cla to pytest * Validate the input to autopytorch * Bug fixes after rebase * Move to new scikit learn * Remove dangerous convert dtype * Try to remove random float error again and make data pickable * Tets pickle on versions higher than 3.6 * Tets pickle on versions higher than 3.6 * Comment fixes * Adding tabular regression pipeline (automl#85) * removed old supported_tasks dictionary from heads, added some docstrings and some small fixes * removed old supported_tasks attribute and updated doc strings in base backbone and base head components * removed old supported_tasks attribute from network backbones * put time series backbones in separate files, add doc strings and refactored search space arguments * split image networks into separate files, add doc strings and refactor search space * fix typo * add an intial simple backbone test similar to the network head test * fix flake8 * fixed imports in backbones and heads * added new network backbone and head tests * enabled tests for adding custom backbones and heads, added required properties to base head and base backbone * adding tabular regression pipeline * fix flake8 * adding tabular regression pipeline * fix flake8 * fix regression test * fix indentation and comments, undo change in base network * pipeline fitting tests now check the expected output shape dynamically based on the input data * refactored trainer tests, added trainer test for regression * remove regression from mixup unitest * use pandas unique instead of numpy * [IMPORTANT] added proper target casting based on task type to base trainer * adding tabular regression task to api * adding tabular regression example, some small fixes * new/more tests for tabular regression * fix mypy and flake8 errors from merge * fix issues with new weighted loss and regression tasks * change tabular column transformer to use net fit_dictionary_tabular fixture * fixing tests, replaced num_classes with output_shape * fixes after merge * adding voting regressor wrapper * fix mypy and flake * updated example * lower r2 target * address comments * increasing timeout * increase number of labels in test_losses because it occasionally failed if one class was not in the labels * lower regression lr in score test until seeding properly works * fix randomization in feature validator test * Make sure the performance of pipeline is at least 0.8 * Early stop fixes * Network Cleanup (automl#81) * removed old supported_tasks dictionary from heads, added some docstrings and some small fixes * removed old supported_tasks attribute and updated doc strings in base backbone and base head components * removed old supported_tasks attribute from network backbones * put time series backbones in separate files, add doc strings and refactored search space arguments * split image networks into separate files, add doc strings and refactor search space * fix typo * add an intial simple backbone test similar to the network head test * fix flake8 * fixed imports in backbones and heads * added new network backbone and head tests * enabled tests for adding custom backbones and heads, added required properties to base head and base backbone * First documentation * Default to ubuntu-18.04 * Comment enhancements * Feature preprocessors, Loss strategies (automl#86) * ADD Weighted loss * Now? * Fix tests, flake, mypy * Fix tests * Fix mypy * change back sklearn requirement * Assert for fast ica sklearn bug * Forgot to add skip * Fix tests, changed num only data to float * removed fast ica * change num only dataset * Increased number of features in num only * Increase timeout for pytest * ADD tensorboard to requirement * Fix bug with small_preprocess * Fix bug in pytest execution * Fix tests * ADD error is raised if default not in include * Added dynamic search space for deciding n components in feature preprocessors, add test for pipeline include * Moved back to random configs in tabular test * Added floor and ceil and handling of logs * Fix flake * Remove TruncatedSVD from cs if num numerical ==1 * ADD flakyness to network accuracy test * fix flake * remove cla to pytest * Validate the input to autopytorch * Bug fixes after rebase * Move to new scikit learn * Remove dangerous convert dtype * Try to remove random float error again and make data pickable * Tets pickle on versions higher than 3.6 * Tets pickle on versions higher than 3.6 * Comment fixes * [REFACTORING]: no change in the functionalities, inputs, returns * Modified an error message * [Test error fix]: Fixed the error caused by flake8 * [Test error fix]: Fixed the error caused by flake8 * FIX weighted loss issue (automl#94) * Changed tests for losses and how weighted strategy is handled in the base trainer * Addressed comments from francisco * Fix training test * Re-arranged tests and moved test_setup to pytest * Reduced search space for dummy forward backward pass of backbones * Fix typo * ADD Doc string to loss function * Logger enhancements * show_models * Move to spawn * Adding missing logger line * Feedback from comments * ADD_109 * No print allow * [PR response]: deleted unneeded changes from merge and fixed the doc-string. * fixed the for loop in type_check based on samuel's review * deleted blank space pointed out by flake8 * Try no autouse * handle nans in categorical columns (automl#118) * handle nans in categorical columns * Fixed error in self dtypes * Addressed comments from francisco * Forgot to commit * Fix flake * Embedding layer (automl#91) * work in progress * in progress * Working network embedding * ADD tests for network embedding * Removed ordinal encoder * Removed ordinal encoder * Add seed for test_losses for reproducibility * Addressed comments * fix flake * fix test import training * ADD_109 * No print allow * Fix tests and move to boston * Debug issue with python 3.6 * Debug for python3.6 * Run only debug file * work in progress * in progress * Working network embedding * ADD tests for network embedding * Removed ordinal encoder * Removed ordinal encoder * Addressed comments * fix flake * fix test import training * Fix tests and move to boston * Debug issue with python 3.6 * Run only debug file * Debug for python3.6 * print paths of parent dir * Trying to run examples * Trying to run examples * Add success model * Added parent directory for printing paths * Try no autouse * print log file to see if backend is saving num run * Setup logger in backend * handle nans in categorical columns (automl#118) * handle nans in categorical columns * Fixed error in self dtypes * Addressed comments from francisco * Forgot to commit * Fix flake * try without embeddings * work in progress * in progress * Working network embedding * ADD tests for network embedding * Removed ordinal encoder * Removed ordinal encoder * Addressed comments * fix flake * fix test import training * Fix tests and move to boston * Debug issue with python 3.6 * Run only debug file * Debug for python3.6 * work in progress * in progress * Working network embedding * ADD tests for network embedding * print paths of parent dir * Trying to run examples * Trying to run examples * Add success model * Added parent directory for printing paths * print log file to see if backend is saving num run * Setup logger in backend * try without embeddings * no embedding for python 3.6 * Deleted debug example * Fix test for evaluation * Deleted utils file Co-authored-by: chico <[email protected]> * Fixes to address automlbenchmark problems * Fix trajectory file output * modified the doc-string in TransformSubset in base_dataset.py * change config_id to config_id+1 (automl#129) * move to a minimization problem (automl#113) * move to a minimization problem * Fix missing test loss file * Missed regression * More robust test * Try signal timeout * Kernel PCA failures * Feedback from Ravin * Better debug msg * Feedback from comments * Doc string request * Feedback from comments * Enhanced doc string * FIX_123 (automl#133) * FIX_123 * Better debug msg * at least 1 config in regression * Return self in _fit() * Adds more examples to customise AutoPyTorch. (automl#124) * 3 examples plus doc update * Forgot the examples * Added example for resampling strategy * Update example worflow * Fixed bugs in example and resampling strategies * Addressed comments * Addressed comments * Addressed comments from shuhei, better documentation * [Feat] Better traditional pipeline cutoff time (automl#141) * [Feat] Better traditional pipeline cutoff time * Fix unit testing * Better failure msg * bug fix catboost * Feedback from Ravin * First batch of feedback from comments * Missed examples * Syntax fix * Hyperparameter Search Space updates now with constant and include ability (automl#146) * In progress, add_hyperparameter * Added SearchSpace working functionality * Working search space update with test for __choice__ and fix flake * fixed mypy bug and bug in making constant float hyperparameters * Add test for fitting pipeline with constant updates * fix flake * bug in int for feature preprocessors and minor bugs in hyperparameter search space fixed * Forgot to add a file * Addressed comments, better documentation and better tests for search space updates * Fix flake * [Bug] Fix random halt problems on traditional pipelines (automl#147) * [feat] Fix random halt problems on traditional pipelines * Documentation update * Fix flake * Flake due to kernel pca errors * Run history traditional (automl#121) * In progress, issue with failed traditional * working traditional classifiers * Addressed comments from francisco * Changed test loop in test_api * Add .autopytorch runs back again * Addressed comments, better documentation and dict for runhistory * Fix flake * Fix tests and add additional run info for crossval * fix tests for train evaluator and api * Addressed comments * Addressed comments * Addressed comments from shuhei, removed deleting from additioninfo * [FIX] Enables backend to track the num run (automl#162) * AA_151 * doc the peek attr * [ADD] Relax constant pipeline performance * [Doc] First push of the developer documentation (automl#127) * First push of the developer documentation * Feedback from Ravin * Document scikit-learn develop guide * Feedback from Ravin * Delete extra point * Refactoring base dataset splitting functions (automl#106) * [Fork from automl#105] Made CrossValFuncs and HoldOutFuncs class to group the functions * Modified time_series_dataset.py to be compatible with resampling_strategy.py * [fix]: back to the renamed version of CROSS_VAL_FN from temporal SplitFunc typing. * fixed flake8 issues in three files * fixed the flake8 issues * [refactor] Address the francisco's comments * [refactor] Adress the francisco's comments * [refactor] Address the doc-string issue in TransformSubset class * [fix] Address flake8 issues * [fix] Fix flake8 issue * [fix] Fix mypy issues raised by github check * [fix] Fix a mypy issue * [fix] Fix a contradiction in holdout_stratified_validation Since stratified splitting requires to shuffle by default and it raises error in the github check, I fixed this issue. * [fix] Address the francisco's review * [fix] Fix a mypy issue tabular_dataset.py * [fix] Address the francisco's comment about the self.dataset_name Since we would to use the dataset name which does not have any name, I decided to get self.dataset_name back to Optional[str]. * [fix] Fix mypy issues * [Fix] Refactor development reproducibility (automl#172) * [Fix] pass random state to randomized algorithms * [Fix] double instantiation of random state * [fix] Flaky for sample configuration * [FIX] Runtime warning * [FIX] hardcoded budget * [FIX] flake * [Fix] try forked * [Fix] try forked * [FIX] budget * [Fix] missing random_state in trainer * [Fix] overwrite in random_state * [FIX] fix seed in splits * [Rebase] * [FIX] Update cv score after split num change * [FIX] CV split * [ADD] Extra visualization example (automl#189) * [ADD] Extra visualization example * Update docs/manual.rst Co-authored-by: Ravin Kohli <[email protected]> * Update docs/manual.rst Co-authored-by: Ravin Kohli <[email protected]> * [Fix] missing version * Update examples/tabular/40_advanced/example_visualization.py Co-authored-by: Ravin Kohli <[email protected]> * [FIX] make docs more clear to the user Co-authored-by: Ravin Kohli <[email protected]> * [Fix] docs links (automl#201) * [Fix] docs links * Update README.md Co-authored-by: Ravin Kohli <[email protected]> * Update examples check * Remove tmp in examples Co-authored-by: Ravin Kohli <[email protected]> * [Refactor] Use the backend implementation from automl common (automl#185) * [ADD] First push to enable common backend * Fix unit test * Try public https * [FIX] conftest prefix * [fix] unit test * [FIX] Fix fixture in score * [Fix] pytest collection * [FIX] flake * [FIX] regression also! * Update README.md Co-authored-by: Ravin Kohli <[email protected]> * Update .gitmodules Co-authored-by: Ravin Kohli <[email protected]> * [FIX] Regression time * Make flaky in case memout doesn't happen * Refacto development automl common backend debug (#2) * [ADD] debug information * [FIX] try fork for more stability Co-authored-by: Ravin Kohli <[email protected]> * [DOC] Adds documentation to the abstract evaluator (automl#160) * DOC_153 * Changes from Ravin * [FIX] improve clarity of msg in commit * [FIX] Update Readme (automl#208) * Reduce run time of the test (automl#205) * In progress, changing te4sts * Reduce time for tests * Fix flake in tests * Patch train in other tests also * Address comments from shuhei and fransisco: * Move base training to pytest * Fix flake in tests * forgot to pass n_samples * stupid error * Address comments from shuhei, remove hardcoding and fix bug in dummy eval function * Skip ensemble test for python >=3.7 and introduce random state for feature processors * fix flake * Remove example workflow * Remove from __init__ in feature preprocessing * [refactor] Getting dataset properties from the dataset object (automl#164) * Use get_required_dataset_info of the dataset when needing required info for getting dataset requirements * Fix flake * Fix bug in getting dataset requirements * Added doc string to explain dataset properties * Update doc string in utils pipeline * Change ubuntu version in docs workflow (automl#237) * Add dist check worflow (automl#238) * [feature] Greedy Portfolio (automl#200) * initial configurations added * In progress, adding flag in search function * Adds documentation, example and fixes setup.py * Address comments from shuhei, change run_greedy to portfolio_selection * address comments from fransisco, movie portfolio to configs * Address comments from fransisco, add tests for greedy portfolio and tests * fix flake tests * Simplify portfolio selection * Update autoPyTorch/optimizer/smbo.py Co-authored-by: Francisco Rivera Valverde <[email protected]> * Address comments from fransisco, path exception handling and test * fix flake * Address comments from shuhei * fix bug in setup.py * fix tests in base trainer evaluate, increase n samples and add seed * fix tests in base trainer evaluate, increase n samples (fix) Co-authored-by: Francisco Rivera Valverde <[email protected]> * [ADD] Forkserver as default multiprocessing strategy (automl#223) * First push of forkserver * [Fix] Missing file * [FIX] mypy * [Fix] renam choice to init * [Fix] Unit test * [Fix] bugs in examples * [Fix] ensemble builder * Update autoPyTorch/pipeline/components/preprocessing/image_preprocessing/normalise/__init__.py Co-authored-by: Ravin Kohli <[email protected]> * Update autoPyTorch/pipeline/components/preprocessing/image_preprocessing/normalise/__init__.py Co-authored-by: Ravin Kohli <[email protected]> * Update autoPyTorch/pipeline/components/preprocessing/tabular_preprocessing/encoding/__init__.py Co-authored-by: Ravin Kohli <[email protected]> * Update autoPyTorch/pipeline/components/preprocessing/image_preprocessing/normalise/__init__.py Co-authored-by: Ravin Kohli <[email protected]> * Update autoPyTorch/pipeline/components/preprocessing/tabular_preprocessing/feature_preprocessing/__init__.py Co-authored-by: Ravin Kohli <[email protected]> * Update autoPyTorch/pipeline/components/preprocessing/tabular_preprocessing/scaling/__init__.py Co-authored-by: Ravin Kohli <[email protected]> * Update autoPyTorch/pipeline/components/setup/network_head/__init__.py Co-authored-by: Ravin Kohli <[email protected]> * Update autoPyTorch/pipeline/components/setup/network_initializer/__init__.py Co-authored-by: Ravin Kohli <[email protected]> * Update autoPyTorch/pipeline/components/setup/network_embedding/__init__.py Co-authored-by: Ravin Kohli <[email protected]> * [FIX] improve doc-strings * Fix rebase Co-authored-by: Ravin Kohli <[email protected]> * [ADD] Get incumbent config (automl#175) * In progress get_incumbent_results * [Add] get_incumbent_results to base task, changed additional info in abstract evaluator, and tests * In progress addressing fransisco's comment * Proper check for include_traditional * Fix flake * Mock search of estimator * Fixed path of run history test_api * Addressed comments from Fransisco, making better tests * fix flake * After rebase fix issues * fix flake * Added debug information for API * filtering only successful runs in get_incumbent_results * Address comments from fransisco * Revert changes made to run history assertion in base taks #1257 * fix flake issue * [ADD] Coverage calculation (automl#224) * [ADD] Coverage calculation * [Fix] Flake8 * [fix] rebase artifacts * [Fix] smac reqs * [Fix] Make traditional test robust * [Fix] unit test * [Fix] test_evaluate * [Fix] Try more time for cross validation * Fix mypy post rebase * Fix unit test * [ADD] Pytest schedule (automl#234) * add schedule for pytests workflow * Add ref to development branch * Add scheduled test * update schedule workflow to run on python 3.8 * omit test, examples, workflow from coverage and remove unnecessary code from schedule * Fix call for python3.8 * Fix call for python3.8 (2) * fix code cov call in python 3.8 * Finally fix cov call * [fix] Dropout bug fix (automl#247) * fix dropout bug * fix dropout shape discrepancy * Fix unit test bug * Add tests for dropout shape asper comments from fransisco * Fix flake * Early stop on metric * Enable long run regression Co-authored-by: Ravin Kohli <[email protected]> Co-authored-by: Ravin Kohli <[email protected]> Co-authored-by: bastiscode <[email protected]> Co-authored-by: nabenabe0928 <[email protected]> Co-authored-by: nabenabe0928 <[email protected]>
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In preparation to move the refactor_development branch to be development, we were missing:
This PR aims to fix this.